Mechanistic Data Science for STEM Education and Applications - Couverture souple

Liu, Wing Kam; Gan, Zhengtao; Fleming, Mark

Couverture souple

ISBN 10 : 3030878341 ISBN 13 : 9783030878344

Editeur : Springer, 2022

Synopsis

This book introduces Mechanistic Data Science (MDS) as a structured methodology for combining data science tools with mathematical scientific principles (i.e., “mechanistic” principles) to solve intractable problems.  Traditional data science methodologies require copious quantities of data to show a reliable pattern, but the amount of required data can be greatly reduced by considering the mathematical science principles. MDS is presented here in six easy-to-follow modules: 1) Multimodal data generation and collection, 2) extraction of mechanistic features, 3) knowledge-driven dimension reduction, 4) reduced order surrogate models, 5) deep learning for regression and classification, and 6) system and design. These data science and mechanistic analysis steps are presented in an intuitive manner that emphasizes practical concepts for solving engineering problems as well as real-life problems. This book is written in a spectral style and is ideal as an entry level textbook for engineering and data science undergraduate and graduate students, practicing scientists and engineers, as well as STEM (Science, Technology, Engineering, Mathematics) high school students and teachers.

A propos du livre

À propos de l'auteur

Dr. Wing Kam Liu is Walter P. Murphy Professor of Mechanical Engineering & Civil and Environmental Engineering and (by courtesy) Materials Science and Engineering, and Director of Global Center on Advanced Material Systems and Simulation (CAMSIM) at Northwestern University in Evanston, Illinois;  Dr. Zhengtao Gan is Research Assistant Professor in the Department of Mechanical Engineering at Northwestern University in Evanston, Illinois; and Dr. Mark Fleming, is the Chief Technical Officer of Fusion Engineering, and an Adjunct Professor in the Department of Mechanical Engineering at Northwestern University in Evanston, Illinois.

À propos de la quatrième de couverture

This book introduces Mechanistic Data Science (MDS) as a structured methodology for combining data science tools with mathematical scientific principles (i.e., “mechanistic” principles) to solve intractable problems.  Traditional data science methodologies require copious quantities of data to show a reliable pattern, but the amount of required data can be greatly reduced by considering the mathematical science principles. MDS is presented here in six easy-to-follow modules: 1) Multimodal data generation and collection, 2) extraction of mechanistic features, 3) knowledge-driven dimension reduction, 4) reduced order surrogate models, 5) deep learning for regression and classification, and 6) system and design. These data science and mechanistic analysis steps are presented in an intuitive manner that emphasizes practical concepts for solving engineering problems as well as real-life problems. This book is written in a spectral style and is ideal as an entry level textbook for engineering and data science undergraduate and graduate students, practicing scientists and engineers, as well as STEM (Science, Technology, Engineering, Mathematics) high school students and teachers.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2022
• Langue: anglais
• ISBN 10: 3030878341
• ISBN 13: 9783030878344
• Reliure: Broché
• Nombre de pages: 276
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne